Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations4999980
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory839.2 MiB
Average record size in memory176.0 B

Variable types

DateTime1
Text2
Numeric12
Categorical6
Boolean1

Alerts

attack_state is highly overall correlated with packets and 1 other fieldsHigh correlation
bytes is highly overall correlated with bytes_per_packetHigh correlation
bytes_per_packet is highly overall correlated with bytesHigh correlation
bytes_ratio is highly overall correlated with duration and 1 other fieldsHigh correlation
day_of_week is highly overall correlated with is_weekendHigh correlation
duration is highly overall correlated with bytes_ratio and 1 other fieldsHigh correlation
is_weekend is highly overall correlated with day_of_weekHigh correlation
packet_size_variance is highly overall correlated with unique_ports_per_sourceHigh correlation
packets is highly overall correlated with attack_stateHigh correlation
packets_per_second is highly overall correlated with bytes_ratio and 1 other fieldsHigh correlation
severity_score is highly overall correlated with attack_stateHigh correlation
unique_ports_per_source is highly overall correlated with packet_size_varianceHigh correlation
attack_state is highly imbalanced (95.0%) Imbalance
severity_score is highly imbalanced (93.1%) Imbalance
connection_frequency is highly imbalanced (> 99.9%) Imbalance
duration is highly skewed (γ1 = 46.32933772) Skewed
packets is highly skewed (γ1 = 40.70191093) Skewed
bytes is highly skewed (γ1 = 43.11985347) Skewed
packets_per_second is highly skewed (γ1 = 76.76960159) Skewed
bytes_ratio is highly skewed (γ1 = 77.377145) Skewed
duration has unique values Unique
packets_per_second has unique values Unique
bytes_ratio has unique values Unique
hour_of_day has 207608 (4.2%) zeros Zeros
day_of_week has 666664 (13.3%) zeros Zeros
packet_size_variance has 3016872 (60.3%) zeros Zeros

Reproduction

Analysis started2025-10-18 19:44:42.859035
Analysis finished2025-10-18 19:47:45.760802
Duration3 minutes and 2.9 seconds
Software versionydata-profiling v4.16.1
Download configurationconfig.json

Variables

Distinct2216417
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
Minimum2025-05-03 00:00:00
Maximum2025-06-01 23:59:59
Invalid dates0
Invalid dates (%)0.0%
2025-10-18T15:47:45.794042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:45.852017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct3433034
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:46.498823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.360341
Min length7

Characters and Unicode

Total characters66801437
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3016872 ?
Unique (%)60.3%

Sample

1st row10.105.90.240
2nd row172.22.51.179
3rd row172.25.234.43
4th row10.209.50.247
5th row172.27.191.134
ValueCountFrequency (%)
192.168.44.210 37
 
< 0.1%
192.168.154.195 36
 
< 0.1%
192.168.129.192 35
 
< 0.1%
192.168.136.66 35
 
< 0.1%
192.168.33.72 35
 
< 0.1%
192.168.126.197 35
 
< 0.1%
192.168.88.128 34
 
< 0.1%
192.168.40.184 34
 
< 0.1%
192.168.254.8 34
 
< 0.1%
192.168.160.45 34
 
< 0.1%
Other values (3433024) 4999631
> 99.9%
2025-10-18T15:47:47.163448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 14999940
22.5%
1 13919207
20.8%
2 9349059
14.0%
0 3926392
 
5.9%
9 3867639
 
5.8%
8 3803637
 
5.7%
6 3797709
 
5.7%
7 3791976
 
5.7%
3 3288203
 
4.9%
4 3126704
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66801437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 14999940
22.5%
1 13919207
20.8%
2 9349059
14.0%
0 3926392
 
5.9%
9 3867639
 
5.8%
8 3803637
 
5.7%
6 3797709
 
5.7%
7 3791976
 
5.7%
3 3288203
 
4.9%
4 3126704
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66801437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 14999940
22.5%
1 13919207
20.8%
2 9349059
14.0%
0 3926392
 
5.9%
9 3867639
 
5.8%
8 3803637
 
5.7%
6 3797709
 
5.7%
7 3791976
 
5.7%
3 3288203
 
4.9%
4 3126704
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66801437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 14999940
22.5%
1 13919207
20.8%
2 9349059
14.0%
0 3926392
 
5.9%
9 3867639
 
5.8%
8 3803637
 
5.7%
6 3797709
 
5.7%
7 3791976
 
5.7%
3 3288203
 
4.9%
4 3126704
 
4.7%
Distinct2487447
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:47.620468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.330234
Min length8

Characters and Unicode

Total characters66650901
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849930 ?
Unique (%)37.0%

Sample

1st row10.2.137.106
2nd row192.168.85.218
3rd row192.168.150.181
4th row10.0.122.90
5th row192.168.237.151
ValueCountFrequency (%)
192.168.242.103 48
 
< 0.1%
192.168.63.158 48
 
< 0.1%
192.168.186.238 47
 
< 0.1%
192.168.90.171 47
 
< 0.1%
192.168.58.83 47
 
< 0.1%
192.168.95.71 47
 
< 0.1%
192.168.198.39 47
 
< 0.1%
192.168.152.40 47
 
< 0.1%
192.168.176.58 46
 
< 0.1%
192.168.129.174 46
 
< 0.1%
Other values (2487437) 4999510
> 99.9%
2025-10-18T15:47:48.090917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 14999940
22.5%
1 14396373
21.6%
2 9580447
14.4%
0 3972671
 
6.0%
7 3926830
 
5.9%
6 3925143
 
5.9%
9 3924462
 
5.9%
8 3923177
 
5.9%
3 2865849
 
4.3%
4 2656729
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66650901
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 14999940
22.5%
1 14396373
21.6%
2 9580447
14.4%
0 3972671
 
6.0%
7 3926830
 
5.9%
6 3925143
 
5.9%
9 3924462
 
5.9%
8 3923177
 
5.9%
3 2865849
 
4.3%
4 2656729
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66650901
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 14999940
22.5%
1 14396373
21.6%
2 9580447
14.4%
0 3972671
 
6.0%
7 3926830
 
5.9%
6 3925143
 
5.9%
9 3924462
 
5.9%
8 3923177
 
5.9%
3 2865849
 
4.3%
4 2656729
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66650901
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 14999940
22.5%
1 14396373
21.6%
2 9580447
14.4%
0 3972671
 
6.0%
7 3926830
 
5.9%
6 3925143
 
5.9%
9 3924462
 
5.9%
8 3923177
 
5.9%
3 2865849
 
4.3%
4 2656729
 
4.0%

source_port
Real number (ℝ)

Distinct64512
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33286.39
Minimum1024
Maximum65535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.149192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile4262
Q117168
median33291
Q349404
95-th percentile62301
Maximum65535
Range64511
Interquartile range (IQR)32236

Descriptive statistics

Standard deviation18617.757
Coefficient of variation (CV)0.55932041
Kurtosis-1.1996344
Mean33286.39
Median Absolute Deviation (MAD)16118
Skewness-0.00032635275
Sum1.6643128 × 1011
Variance3.4662088 × 108
MonotonicityNot monotonic
2025-10-18T15:47:48.204995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29024 120
 
< 0.1%
49630 116
 
< 0.1%
12849 115
 
< 0.1%
42860 115
 
< 0.1%
2997 113
 
< 0.1%
21236 112
 
< 0.1%
30515 112
 
< 0.1%
29041 111
 
< 0.1%
58211 111
 
< 0.1%
18906 111
 
< 0.1%
Other values (64502) 4998844
> 99.9%
ValueCountFrequency (%)
1024 72
< 0.1%
1025 65
< 0.1%
1026 70
< 0.1%
1027 72
< 0.1%
1028 66
< 0.1%
1029 79
< 0.1%
1030 81
< 0.1%
1031 77
< 0.1%
1032 70
< 0.1%
1033 80
< 0.1%
ValueCountFrequency (%)
65535 77
< 0.1%
65534 68
< 0.1%
65533 92
< 0.1%
65532 80
< 0.1%
65531 86
< 0.1%
65530 89
< 0.1%
65529 73
< 0.1%
65528 84
< 0.1%
65527 79
< 0.1%
65526 78
< 0.1%

dest_port
Real number (ℝ)

Distinct27815
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1302.3912
Minimum1
Maximum65534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.260352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q125
median53
Q3443
95-th percentile5432
Maximum65534
Range65533
Interquartile range (IQR)418

Descriptive statistics

Standard deviation4990.2454
Coefficient of variation (CV)3.8316027
Kurtosis49.920678
Mean1302.3912
Median Absolute Deviation (MAD)31
Skewness6.4915129
Sum6.5119297 × 109
Variance24902550
MonotonicityNot monotonic
2025-10-18T15:47:48.314794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 795338
15.9%
22 795025
15.9%
443 794432
15.9%
53 789482
15.8%
25 786207
15.7%
21 107179
 
2.1%
3389 106363
 
2.1%
1433 99752
 
2.0%
23 99399
 
2.0%
3306 98968
 
2.0%
Other values (27805) 527835
10.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
4 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
15 1
 
< 0.1%
21 107179
 
2.1%
22 795025
15.9%
ValueCountFrequency (%)
65534 1
 
< 0.1%
65533 1
 
< 0.1%
65527 1
 
< 0.1%
65525 3
< 0.1%
65523 1
 
< 0.1%
65520 1
 
< 0.1%
65519 2
< 0.1%
65518 1
 
< 0.1%
65517 1
 
< 0.1%
65516 2
< 0.1%

protocol
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
TCP
1667838 
UDP
1666661 
ICMP
1665481 

Length

Max length4
Median length3
Mean length3.3330975
Min length3

Characters and Unicode

Total characters16665421
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTCP
2nd rowUDP
3rd rowTCP
4th rowTCP
5th rowICMP

Common Values

ValueCountFrequency (%)
TCP 1667838
33.4%
UDP 1666661
33.3%
ICMP 1665481
33.3%

Length

2025-10-18T15:47:48.368386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:47:48.406587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp 1667838
33.4%
udp 1666661
33.3%
icmp 1665481
33.3%

Most occurring characters

ValueCountFrequency (%)
P 4999980
30.0%
C 3333319
20.0%
T 1667838
 
10.0%
U 1666661
 
10.0%
D 1666661
 
10.0%
I 1665481
 
10.0%
M 1665481
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16665421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 4999980
30.0%
C 3333319
20.0%
T 1667838
 
10.0%
U 1666661
 
10.0%
D 1666661
 
10.0%
I 1665481
 
10.0%
M 1665481
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16665421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 4999980
30.0%
C 3333319
20.0%
T 1667838
 
10.0%
U 1666661
 
10.0%
D 1666661
 
10.0%
I 1665481
 
10.0%
M 1665481
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16665421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 4999980
30.0%
C 3333319
20.0%
T 1667838
 
10.0%
U 1666661
 
10.0%
D 1666661
 
10.0%
I 1665481
 
10.0%
M 1665481
 
10.0%

duration
Real number (ℝ)

High correlation  Skewed  Unique 

Distinct4999980
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.147608
Minimum0.0010115253
Maximum3599.9557
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.458882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0010115253
5-th percentile1.2988664
Q13.6775219
median7.3115413
Q314.455591
95-th percentile38.276362
Maximum3599.9557
Range3599.9547
Interquartile range (IQR)10.778069

Descriptive statistics

Standard deviation53.448568
Coefficient of variation (CV)4.0652695
Kurtosis2487.9606
Mean13.147608
Median Absolute Deviation (MAD)4.4366334
Skewness46.329338
Sum65737775
Variance2856.7494
MonotonicityNot monotonic
2025-10-18T15:47:48.515868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.735717535 1
 
< 0.1%
3.364890537 1
 
< 0.1%
14.68641517 1
 
< 0.1%
1.655156466 1
 
< 0.1%
4.358016881 1
 
< 0.1%
1.840864708 1
 
< 0.1%
2.485732715 1
 
< 0.1%
33.61791684 1
 
< 0.1%
13.41163834 1
 
< 0.1%
17.62174242 1
 
< 0.1%
Other values (4999970) 4999970
> 99.9%
ValueCountFrequency (%)
0.001011525325 1
< 0.1%
0.001012573054 1
< 0.1%
0.001014164959 1
< 0.1%
0.001018804127 1
< 0.1%
0.001026028399 1
< 0.1%
0.001029666659 1
< 0.1%
0.001035507737 1
< 0.1%
0.001039009023 1
< 0.1%
0.001046861058 1
< 0.1%
0.001064261638 1
< 0.1%
ValueCountFrequency (%)
3599.955683 1
< 0.1%
3599.07674 1
< 0.1%
3598.129083 1
< 0.1%
3597.526493 1
< 0.1%
3597.041353 1
< 0.1%
3595.984312 1
< 0.1%
3592.989136 1
< 0.1%
3592.111057 1
< 0.1%
3590.828593 1
< 0.1%
3586.761133 1
< 0.1%

packets
Real number (ℝ)

High correlation  Skewed 

Distinct897
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.201782
Minimum1
Maximum10289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.573740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q145
median50
Q355
95-th percentile62
Maximum10289
Range10288
Interquartile range (IQR)10

Descriptive statistics

Standard deviation236.04302
Coefficient of variation (CV)4.1264977
Kurtosis1706.4905
Mean57.201782
Median Absolute Deviation (MAD)5
Skewness40.701911
Sum2.8600777 × 108
Variance55716.308
MonotonicityNot monotonic
2025-10-18T15:47:48.629992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 277015
 
5.5%
49 276714
 
5.5%
51 270541
 
5.4%
48 270090
 
5.4%
52 261067
 
5.2%
47 260028
 
5.2%
53 246240
 
4.9%
46 244381
 
4.9%
54 227675
 
4.6%
45 225468
 
4.5%
Other values (887) 2440761
48.8%
ValueCountFrequency (%)
1 1464
 
< 0.1%
2 2952
0.1%
3 5131
0.1%
4 6357
0.1%
5 6414
0.1%
6 5321
0.1%
7 3756
0.1%
8 2360
 
< 0.1%
9 1430
 
< 0.1%
10 738
 
< 0.1%
ValueCountFrequency (%)
10289 1
 
< 0.1%
10285 1
 
< 0.1%
10278 1
 
< 0.1%
10276 1
 
< 0.1%
10275 1
 
< 0.1%
10271 1
 
< 0.1%
10270 2
< 0.1%
10266 3
< 0.1%
10261 1
 
< 0.1%
10252 1
 
< 0.1%

bytes
Real number (ℝ)

High correlation  Skewed 

Distinct96008
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47322.729
Minimum1
Maximum18221935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.684521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21005
Q131657
median39220
Q347347
95-th percentile60394
Maximum18221935
Range18221934
Interquartile range (IQR)15690

Descriptive statistics

Standard deviation326633.16
Coefficient of variation (CV)6.902247
Kurtosis1869.6576
Mean47322.729
Median Absolute Deviation (MAD)7829
Skewness43.119853
Sum2.366127 × 1011
Variance1.0668922 × 1011
MonotonicityNot monotonic
2025-10-18T15:47:48.742727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 237
 
< 0.1%
38645 232
 
< 0.1%
37880 224
 
< 0.1%
37130 222
 
< 0.1%
37386 222
 
< 0.1%
37195 219
 
< 0.1%
36192 218
 
< 0.1%
38777 217
 
< 0.1%
37540 216
 
< 0.1%
34373 216
 
< 0.1%
Other values (95998) 4997757
> 99.9%
ValueCountFrequency (%)
1 237
< 0.1%
28 1
 
< 0.1%
30 1
 
< 0.1%
33 2
 
< 0.1%
36 2
 
< 0.1%
37 2
 
< 0.1%
38 2
 
< 0.1%
39 11
 
< 0.1%
40 8
 
< 0.1%
41 8
 
< 0.1%
ValueCountFrequency (%)
18221935 1
< 0.1%
17955995 1
< 0.1%
17274008 1
< 0.1%
17251106 1
< 0.1%
17155495 1
< 0.1%
17144238 1
< 0.1%
17094607 1
< 0.1%
17070519 1
< 0.1%
17050082 1
< 0.1%
16982759 1
< 0.1%

bytes_per_packet
Real number (ℝ)

High correlation 

Distinct1123214
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean791.49458
Minimum1
Maximum2222.956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.802033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile442.41509
Q1658.59459
median796.98246
Q3933.74
95-th percentile1131
Maximum2222.956
Range2221.956
Interquartile range (IQR)275.14541

Descriptive statistics

Standard deviation216.27265
Coefficient of variation (CV)0.27324589
Kurtosis0.83642811
Mean791.49458
Median Absolute Deviation (MAD)137.5731
Skewness-0.31583633
Sum3.9574571 × 109
Variance46773.857
MonotonicityNot monotonic
2025-10-18T15:47:48.855250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 410
 
< 0.1%
1 389
 
< 0.1%
63 380
 
< 0.1%
67 369
 
< 0.1%
65 362
 
< 0.1%
62 361
 
< 0.1%
68 342
 
< 0.1%
60 338
 
< 0.1%
66 333
 
< 0.1%
59 324
 
< 0.1%
Other values (1123204) 4996372
99.9%
ValueCountFrequency (%)
1 389
< 0.1%
1.039215686 1
 
< 0.1%
1.5 1
 
< 0.1%
1.923076923 1
 
< 0.1%
1.941176471 1
 
< 0.1%
2.37254902 1
 
< 0.1%
2.957446809 1
 
< 0.1%
3.109090909 1
 
< 0.1%
3.12244898 1
 
< 0.1%
3.222222222 1
 
< 0.1%
ValueCountFrequency (%)
2222.956044 1
< 0.1%
2182.272727 1
< 0.1%
2173.820513 1
< 0.1%
2173.690722 1
< 0.1%
2136.071429 1
< 0.1%
2130.419355 1
< 0.1%
2123.322581 1
< 0.1%
2120.870968 1
< 0.1%
2105.206897 1
< 0.1%
2094.357143 1
< 0.1%

packets_per_second
Real number (ℝ)

High correlation  Skewed  Unique 

Distinct4999980
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.83007
Minimum0.022609253
Maximum1020544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:48.915878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.022609253
5-th percentile1.2547936
Q13.3970991
median6.7631247
Q313.569023
95-th percentile38.752433
Maximum1020544
Range1020544
Interquartile range (IQR)10.171923

Descriptive statistics

Standard deviation6406.8403
Coefficient of variation (CV)35.430171
Kurtosis7711.8233
Mean180.83007
Median Absolute Deviation (MAD)4.1250202
Skewness76.769602
Sum9.0414674 × 108
Variance41047603
MonotonicityNot monotonic
2025-10-18T15:47:48.974147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.190019611 1
 
< 0.1%
14.56213789 1
 
< 0.1%
3.813047591 1
 
< 0.1%
32.62531435 1
 
< 0.1%
11.70257055 1
 
< 0.1%
27.70437164 1
 
< 0.1%
18.50560992 1
 
< 0.1%
1.546794236 1
 
< 0.1%
3.877229514 1
 
< 0.1%
3.745373098 1
 
< 0.1%
Other values (4999970) 4999970
> 99.9%
ValueCountFrequency (%)
0.0226092526 1
< 0.1%
0.04985491684 1
< 0.1%
0.05307970834 1
< 0.1%
0.05872328551 1
< 0.1%
0.05883796339 1
< 0.1%
0.06155409163 1
< 0.1%
0.06374213069 1
< 0.1%
0.0659931909 1
< 0.1%
0.06612578444 1
< 0.1%
0.06718721909 1
< 0.1%
ValueCountFrequency (%)
1020544.036 1
< 0.1%
1008093.241 1
< 0.1%
1002878.682 1
< 0.1%
988570.6476 1
< 0.1%
985804.1731 1
< 0.1%
965838.2545 1
< 0.1%
955139.2546 1
< 0.1%
951629.7149 1
< 0.1%
946095.937 1
< 0.1%
942173.7323 1
< 0.1%

tcp_flags
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
FIN
834942 
SYN
834226 
URG
834047 
PSH
832843 
ACK
831985 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14999940
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRST
2nd rowSYN
3rd rowSYN
4th rowFIN
5th rowRST

Common Values

ValueCountFrequency (%)
FIN 834942
16.7%
SYN 834226
16.7%
URG 834047
16.7%
PSH 832843
16.7%
ACK 831985
16.6%
RST 831937
16.6%

Length

2025-10-18T15:47:49.025424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:47:49.067500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
fin 834942
16.7%
syn 834226
16.7%
urg 834047
16.7%
psh 832843
16.7%
ack 831985
16.6%
rst 831937
16.6%

Most occurring characters

ValueCountFrequency (%)
S 2499006
16.7%
N 1669168
11.1%
R 1665984
11.1%
F 834942
 
5.6%
I 834942
 
5.6%
Y 834226
 
5.6%
U 834047
 
5.6%
G 834047
 
5.6%
P 832843
 
5.6%
H 832843
 
5.6%
Other values (4) 3327892
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14999940
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2499006
16.7%
N 1669168
11.1%
R 1665984
11.1%
F 834942
 
5.6%
I 834942
 
5.6%
Y 834226
 
5.6%
U 834047
 
5.6%
G 834047
 
5.6%
P 832843
 
5.6%
H 832843
 
5.6%
Other values (4) 3327892
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14999940
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2499006
16.7%
N 1669168
11.1%
R 1665984
11.1%
F 834942
 
5.6%
I 834942
 
5.6%
Y 834226
 
5.6%
U 834047
 
5.6%
G 834047
 
5.6%
P 832843
 
5.6%
H 832843
 
5.6%
Other values (4) 3327892
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14999940
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2499006
16.7%
N 1669168
11.1%
R 1665984
11.1%
F 834942
 
5.6%
I 834942
 
5.6%
Y 834226
 
5.6%
U 834047
 
5.6%
G 834047
 
5.6%
P 832843
 
5.6%
H 832843
 
5.6%
Other values (4) 3327892
22.2%

service
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
HTTP
795338 
SSH
795025 
HTTPS
794432 
DNS
789482 
SMTP
786207 
Other values (11)
1039496 

Length

Max length10
Median length7
Mean length4.1360277
Min length3

Characters and Unicode

Total characters20680056
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMongoDB
2nd rowSMTP
3rd rowSMTP
4th rowHTTPS
5th rowHTTPS

Common Values

ValueCountFrequency (%)
HTTP 795338
15.9%
SSH 795025
15.9%
HTTPS 794432
15.9%
DNS 789482
15.8%
SMTP 786207
15.7%
FTP 107179
 
2.1%
RDP 106363
 
2.1%
MSSQL 99752
 
2.0%
Telnet 99399
 
2.0%
MySQL 98968
 
2.0%
Other values (6) 527835
10.6%

Length

2025-10-18T15:47:49.122677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http 795338
15.9%
ssh 795025
15.9%
https 794432
15.9%
dns 789482
15.8%
smtp 786207
15.7%
ftp 107179
 
2.1%
rdp 106363
 
2.1%
mssql 99752
 
2.0%
telnet 99399
 
2.0%
mysql 98968
 
2.0%
Other values (6) 527835
10.6%

Most occurring characters

ValueCountFrequency (%)
S 4554444
22.0%
T 4172325
20.2%
P 2984200
14.4%
H 2384795
11.5%
M 1181244
 
5.7%
D 993421
 
4.8%
N 789482
 
3.8%
e 395262
 
1.9%
o 329506
 
1.6%
n 305497
 
1.5%
Other values (20) 2589880
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20680056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 4554444
22.0%
T 4172325
20.2%
P 2984200
14.4%
H 2384795
11.5%
M 1181244
 
5.7%
D 993421
 
4.8%
N 789482
 
3.8%
e 395262
 
1.9%
o 329506
 
1.6%
n 305497
 
1.5%
Other values (20) 2589880
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20680056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 4554444
22.0%
T 4172325
20.2%
P 2984200
14.4%
H 2384795
11.5%
M 1181244
 
5.7%
D 993421
 
4.8%
N 789482
 
3.8%
e 395262
 
1.9%
o 329506
 
1.6%
n 305497
 
1.5%
Other values (20) 2589880
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20680056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 4554444
22.0%
T 4172325
20.2%
P 2984200
14.4%
H 2384795
11.5%
M 1181244
 
5.7%
D 993421
 
4.8%
N 789482
 
3.8%
e 395262
 
1.9%
o 329506
 
1.6%
n 305497
 
1.5%
Other values (20) 2589880
12.5%

attack_state
Categorical

High correlation  Imbalance 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
Normal
4908379 
Port_Scan
 
36181
Brute_Force
 
21792
DDoS
 
10789
Phishing
 
7233
Other values (7)
 
15606

Length

Max length17
Median length6
Mean length6.0600656
Min length3

Characters and Unicode

Total characters30300207
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 4908379
98.2%
Port_Scan 36181
 
0.7%
Brute_Force 21792
 
0.4%
DDoS 10789
 
0.2%
Phishing 7233
 
0.1%
SQL_Injection 5360
 
0.1%
Malware 3648
 
0.1%
Data_Exfiltration 2714
 
0.1%
Insider_Threat 1839
 
< 0.1%
Ransomware 1166
 
< 0.1%
Other values (2) 879
 
< 0.1%

Length

2025-10-18T15:47:49.173898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
normal 4908379
98.2%
port_scan 36181
 
0.7%
brute_force 21792
 
0.4%
ddos 10789
 
0.2%
phishing 7233
 
0.1%
sql_injection 5360
 
0.1%
malware 3648
 
0.1%
data_exfiltration 2714
 
0.1%
insider_threat 1839
 
< 0.1%
ransomware 1166
 
< 0.1%
Other values (2) 879
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 4999785
16.5%
o 4986816
16.5%
a 4964604
16.4%
l 4914741
16.2%
m 4909545
16.2%
N 4908379
16.2%
t 73314
 
0.2%
_ 68321
 
0.2%
c 63333
 
0.2%
n 59853
 
0.2%
Other values (26) 351516
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30300207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 4999785
16.5%
o 4986816
16.5%
a 4964604
16.4%
l 4914741
16.2%
m 4909545
16.2%
N 4908379
16.2%
t 73314
 
0.2%
_ 68321
 
0.2%
c 63333
 
0.2%
n 59853
 
0.2%
Other values (26) 351516
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30300207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 4999785
16.5%
o 4986816
16.5%
a 4964604
16.4%
l 4914741
16.2%
m 4909545
16.2%
N 4908379
16.2%
t 73314
 
0.2%
_ 68321
 
0.2%
c 63333
 
0.2%
n 59853
 
0.2%
Other values (26) 351516
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30300207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 4999785
16.5%
o 4986816
16.5%
a 4964604
16.4%
l 4914741
16.2%
m 4909545
16.2%
N 4908379
16.2%
t 73314
 
0.2%
_ 68321
 
0.2%
c 63333
 
0.2%
n 59853
 
0.2%
Other values (26) 351516
 
1.2%

severity_score
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
1
4908379 
2
 
43414
3
 
30800
4
 
15342
5
 
2045

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4999980
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4908379
98.2%
2 43414
 
0.9%
3 30800
 
0.6%
4 15342
 
0.3%
5 2045
 
< 0.1%

Length

2025-10-18T15:47:49.220999image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:47:49.259897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4908379
98.2%
2 43414
 
0.9%
3 30800
 
0.6%
4 15342
 
0.3%
5 2045
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4908379
98.2%
2 43414
 
0.9%
3 30800
 
0.6%
4 15342
 
0.3%
5 2045
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4999980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4908379
98.2%
2 43414
 
0.9%
3 30800
 
0.6%
4 15342
 
0.3%
5 2045
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4999980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4908379
98.2%
2 43414
 
0.9%
3 30800
 
0.6%
4 15342
 
0.3%
5 2045
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4999980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4908379
98.2%
2 43414
 
0.9%
3 30800
 
0.6%
4 15342
 
0.3%
5 2045
 
< 0.1%

is_weekend
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
False
3333320 
True
1666660 
ValueCountFrequency (%)
False 3333320
66.7%
True 1666660
33.3%
2025-10-18T15:47:49.296033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

hour_of_day
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.499219
Minimum0
Maximum23
Zeros207608
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:49.333574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation6.9202073
Coefficient of variation (CV)0.60179804
Kurtosis-1.2035876
Mean11.499219
Median Absolute Deviation (MAD)6
Skewness0.00015552093
Sum57495864
Variance47.889269
MonotonicityNot monotonic
2025-10-18T15:47:49.378806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10 209046
 
4.2%
8 208925
 
4.2%
11 208894
 
4.2%
2 208829
 
4.2%
15 208795
 
4.2%
21 208772
 
4.2%
12 208615
 
4.2%
5 208578
 
4.2%
17 208576
 
4.2%
3 208545
 
4.2%
Other values (14) 2912405
58.2%
ValueCountFrequency (%)
0 207608
4.2%
1 208479
4.2%
2 208829
4.2%
3 208545
4.2%
4 207956
4.2%
5 208578
4.2%
6 207628
4.2%
7 207921
4.2%
8 208925
4.2%
9 208184
4.2%
ValueCountFrequency (%)
23 207978
4.2%
22 208039
4.2%
21 208772
4.2%
20 207643
4.2%
19 208263
4.2%
18 208523
4.2%
17 208576
4.2%
16 207689
4.2%
15 208795
4.2%
14 208435
4.2%

day_of_week
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1666667
Minimum0
Maximum6
Zeros666664
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:49.418235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0344261
Coefficient of variation (CV)0.64245036
Kurtosis-1.2906987
Mean3.1666667
Median Absolute Deviation (MAD)2
Skewness-0.10776477
Sum15833270
Variance4.1388897
MonotonicityNot monotonic
2025-10-18T15:47:49.458351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 833330
16.7%
6 833330
16.7%
0 666664
13.3%
1 666664
13.3%
2 666664
13.3%
3 666664
13.3%
4 666664
13.3%
ValueCountFrequency (%)
0 666664
13.3%
1 666664
13.3%
2 666664
13.3%
3 666664
13.3%
4 666664
13.3%
5 833330
16.7%
6 833330
16.7%
ValueCountFrequency (%)
6 833330
16.7%
5 833330
16.7%
4 666664
13.3%
3 666664
13.3%
2 666664
13.3%
1 666664
13.3%
0 666664
13.3%

bytes_ratio
Real number (ℝ)

High correlation  Skewed  Unique 

Distinct4999980
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.329057
Minimum0.00077396691
Maximum35179.454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:49.512960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00077396691
5-th percentile0.8881792
Q12.5523234
median5.2004571
Q310.572009
95-th percentile29.722031
Maximum35179.454
Range35179.453
Interquartile range (IQR)8.019686

Descriptive statistics

Standard deviation205.90119
Coefficient of variation (CV)14.369487
Kurtosis7898.8186
Mean14.329057
Median Absolute Deviation (MAD)3.244647
Skewness77.377145
Sum71644997
Variance42395.299
MonotonicityNot monotonic
2025-10-18T15:47:49.570406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.727319505 1
 
< 0.1%
13.4096487 1
 
< 0.1%
2.868841681 1
 
< 0.1%
4.806192141 1
 
< 0.1%
10.81638766 1
 
< 0.1%
25.75909017 1
 
< 0.1%
15.51695392 1
 
< 0.1%
0.8220318984 1
 
< 0.1%
3.416733947 1
 
< 0.1%
1.597685367 1
 
< 0.1%
Other values (4999970) 4999970
> 99.9%
ValueCountFrequency (%)
0.0007739669074 1
< 0.1%
0.0008669130299 1
< 0.1%
0.001116436643 1
< 0.1%
0.001278565573 1
< 0.1%
0.00130097255 1
< 0.1%
0.001535710108 1
< 0.1%
0.00177247783 1
< 0.1%
0.001917395042 1
< 0.1%
0.002154021645 1
< 0.1%
0.002224363532 1
< 0.1%
ValueCountFrequency (%)
35179.45357 1
< 0.1%
34441.09304 1
< 0.1%
33110.47206 1
< 0.1%
32682.85137 1
< 0.1%
32612.40972 1
< 0.1%
32353.43131 1
< 0.1%
32227.49881 1
< 0.1%
31791.83169 1
< 0.1%
31246.24587 1
< 0.1%
31242.3175 1
< 0.1%

packet_size_variance
Real number (ℝ)

High correlation  Zeros 

Distinct415199
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.272139
Minimum0
Maximum944.15345
Zeros3016872
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:49.627736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3171.20991
95-th percentile250.18133
Maximum944.15345
Range944.15345
Interquartile range (IQR)171.20991

Descriptive statistics

Standard deviation102.48301
Coefficient of variation (CV)1.3986627
Kurtosis0.41969307
Mean73.272139
Median Absolute Deviation (MAD)0
Skewness1.1015738
Sum3.6635923 × 108
Variance10502.768
MonotonicityNot monotonic
2025-10-18T15:47:49.682635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3016872
60.3%
219.0670142 37
 
< 0.1%
239.7810508 36
 
< 0.1%
151.4533622 35
 
< 0.1%
196.5699188 35
 
< 0.1%
187.3786541 35
 
< 0.1%
166.7480782 35
 
< 0.1%
206.8719459 34
 
< 0.1%
220.7260971 34
 
< 0.1%
216.2337802 34
 
< 0.1%
Other values (415189) 1982793
39.7%
ValueCountFrequency (%)
0 3016872
60.3%
0.0005269052021 2
 
< 0.1%
0.0008338523363 2
 
< 0.1%
0.00187065286 2
 
< 0.1%
0.002925959095 2
 
< 0.1%
0.003613360124 2
 
< 0.1%
0.005504204316 2
 
< 0.1%
0.006505870327 2
 
< 0.1%
0.008406164532 2
 
< 0.1%
0.008417937871 2
 
< 0.1%
ValueCountFrequency (%)
944.153451 2
< 0.1%
941.8015946 2
< 0.1%
931.2554903 2
< 0.1%
908.3061271 2
< 0.1%
905.9918128 2
< 0.1%
883.0140918 2
< 0.1%
855.7684015 2
< 0.1%
853.9402034 2
< 0.1%
851.353072 2
< 0.1%
843.439494 2
< 0.1%

connection_frequency
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.1 MiB
1
4999940 
2
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4999980
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4999940
> 99.9%
2 40
 
< 0.1%

Length

2025-10-18T15:47:49.732317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T15:47:49.768401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4999940
> 99.9%
2 40
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4999940
> 99.9%
2 40
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4999980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 4999940
> 99.9%
2 40
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4999980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 4999940
> 99.9%
2 40
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4999980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 4999940
> 99.9%
2 40
 
< 0.1%

unique_ports_per_source
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7834485
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.1 MiB
2025-10-18T15:47:49.802435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile9
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9063984
Coefficient of variation (CV)1.0441718
Kurtosis0.43769716
Mean2.7834485
Median Absolute Deviation (MAD)0
Skewness1.4015577
Sum13917187
Variance8.4471517
MonotonicityNot monotonic
2025-10-18T15:47:49.848473image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 3087074
61.7%
2 516636
 
10.3%
8 289554
 
5.8%
7 268432
 
5.4%
9 210502
 
4.2%
3 196432
 
3.9%
6 157273
 
3.1%
10 108537
 
2.2%
5 59692
 
1.2%
4 55431
 
1.1%
Other values (4) 50417
 
1.0%
ValueCountFrequency (%)
1 3087074
61.7%
2 516636
 
10.3%
3 196432
 
3.9%
4 55431
 
1.1%
5 59692
 
1.2%
6 157273
 
3.1%
7 268432
 
5.4%
8 289554
 
5.8%
9 210502
 
4.2%
10 108537
 
2.2%
ValueCountFrequency (%)
14 192
 
< 0.1%
13 1507
 
< 0.1%
12 10202
 
0.2%
11 38516
 
0.8%
10 108537
 
2.2%
9 210502
4.2%
8 289554
5.8%
7 268432
5.4%
6 157273
3.1%
5 59692
 
1.2%

Interactions

2025-10-18T15:47:31.657332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:01.553701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:04.449278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:07.171757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:09.848264image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:12.488534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:15.252822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:18.142214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:20.810956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:23.510335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:26.156192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:28.850393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:31.891826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:01.798139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:04.673338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:07.395915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:10.069705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:12.711591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:15.497918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:18.364579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:21.033657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:23.731983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:26.379559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:29.083570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:32.126367image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:02.040687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:04.901261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:07.615155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:10.291067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:12.935052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:15.741301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:18.589794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:21.310935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:23.954635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:26.605848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:29.318679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:32.361912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:02.284382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:05.130368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:07.839545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:10.506548image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:13.160479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:15.986146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:18.814243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:21.533180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:24.177539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:26.832792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:29.552995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:32.591351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:02.523358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:05.355755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:08.062173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:10.726339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:13.373542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:16.226174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:19.035120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:21.752961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:24.398351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:27.055250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:29.784079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:32.831848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:02.772684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:05.586339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:08.287706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:10.948747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:13.617392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:16.467916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:19.259808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:21.978755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:24.621637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:27.280375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:30.023509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:33.061877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:03.012132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:05.813201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:08.511137image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:11.167520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:13.849310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:16.706418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:19.475275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:22.199406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:24.841551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:27.504242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:30.253994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:33.299091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:03.256327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:06.043428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:08.734420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:11.389380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:14.093458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:16.951214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:19.697829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:22.411966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:25.063857image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:27.728324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:30.490587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:33.530476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:03.493489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:06.269450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:08.956404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:11.608281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:14.332627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:17.189333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:19.919751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:22.629261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:25.274446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:27.950438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:30.724669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:33.766243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:03.737816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:06.498719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:09.182234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:11.830713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:14.563482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:17.432374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:20.144365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:22.850698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:25.497652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:28.168538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:30.958813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:34.007110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:03.982853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:06.727329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:09.407780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:12.052195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:14.787290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:17.678804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:20.369689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:23.075927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:25.719345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:28.393873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:31.190779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:34.232080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:04.223474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:06.951501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:09.626254image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:12.270293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:15.005665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:17.924233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:20.586717image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:23.294827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:25.936556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:28.612071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-10-18T15:47:31.423209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-10-18T15:47:49.891346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
attack_statebytesbytes_per_packetbytes_ratioconnection_frequencyday_of_weekdest_portdurationhour_of_dayis_weekendpacket_size_variancepacketspackets_per_secondprotocolserviceseverity_scoresource_porttcp_flagsunique_ports_per_source
attack_state1.0000.4470.3250.1330.0000.0000.2990.3230.0380.0000.0330.7640.1390.0000.3111.0000.0000.0010.028
bytes0.4471.0000.8580.2800.000-0.000-0.0190.019-0.0040.0000.0100.4900.0550.0000.0180.2100.0000.0000.012
bytes_per_packet0.3250.8581.0000.2410.0000.000-0.0190.022-0.0050.0010.0160.029-0.0160.0010.2380.4070.0010.0010.015
bytes_ratio0.1330.2800.2411.0000.000-0.0000.008-0.936-0.0000.000-0.0030.1480.9520.0000.0090.167-0.0010.000-0.001
connection_frequency0.0000.0000.0000.0001.0000.0000.0000.0000.0010.0000.0040.0000.0000.0000.0000.0000.0000.0010.006
day_of_week0.000-0.0000.000-0.0000.0001.000-0.0000.000-0.0001.0000.001-0.001-0.0000.0000.0010.000-0.0010.0000.001
dest_port0.299-0.019-0.0190.0080.000-0.0001.000-0.0260.0030.001-0.008-0.0170.0260.0000.4430.4090.0000.0000.001
duration0.3230.0190.022-0.9360.0000.000-0.0261.000-0.0030.0010.0110.010-0.9860.0000.0130.2030.0010.0000.011
hour_of_day0.038-0.004-0.005-0.0000.001-0.0000.003-0.0031.0000.000-0.003-0.0030.0030.0000.0240.0570.0010.000-0.002
is_weekend0.0000.0000.0010.0000.0001.0000.0010.0010.0001.0000.0000.0000.0000.0000.0020.0000.0000.0010.000
packet_size_variance0.0330.0100.016-0.0030.0040.001-0.0080.011-0.0030.0001.0000.010-0.0100.0000.0210.0500.0010.0000.919
packets0.7640.4900.0290.1480.000-0.001-0.0170.010-0.0030.0000.0101.0000.1300.0000.0340.384-0.0010.0010.009
packets_per_second0.1390.055-0.0160.9520.000-0.0000.026-0.9860.0030.000-0.0100.1301.0000.0000.0090.174-0.0010.000-0.010
protocol0.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0010.0000.0000.0000.000
service0.3110.0180.2380.0090.0000.0010.4430.0130.0240.0020.0210.0340.0090.0011.0000.4660.0000.0000.031
severity_score1.0000.2100.4070.1670.0000.0000.4090.2030.0570.0000.0500.3840.1740.0000.4661.0000.0000.0010.042
source_port0.0000.0000.001-0.0010.000-0.0010.0000.0010.0010.0000.001-0.001-0.0010.0000.0000.0001.0000.0000.001
tcp_flags0.0010.0000.0010.0000.0010.0000.0000.0000.0000.0010.0000.0010.0000.0000.0000.0010.0001.0000.000
unique_ports_per_source0.0280.0120.015-0.0010.0060.0010.0010.011-0.0020.0000.9190.009-0.0100.0000.0310.0420.0010.0001.000

Missing values

2025-10-18T15:47:34.856322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-18T15:47:37.314513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestampsource_ipdest_ipsource_portdest_portprotocoldurationpacketsbytesbytes_per_packetpackets_per_secondtcp_flagsserviceattack_stateseverity_scoreis_weekendhour_of_dayday_of_weekbytes_ratiopacket_size_varianceconnection_frequencyunique_ports_per_source
02025-05-03 00:00:0010.105.90.24010.2.137.1063079727017TCP9.73571870752311074.7285717.190020RSTMongoDBNormal1True057.7273200.00000011
12025-05-03 00:00:00172.22.51.179192.168.85.2184385225UDP11.6165644730801655.3404264.045947SYNSMTPNormal1True052.651473129.05103812
22025-05-03 00:00:01172.25.234.43192.168.150.1812195625TCP21.21672957579411016.5087722.686559SYNSMTPNormal1True052.730911217.51369211
32025-05-03 00:00:0110.209.50.24710.0.122.9036284443TCP1.4578455842479732.39655239.784741FINHTTPSNormal1True0529.1382070.00000011
42025-05-03 00:00:02172.27.191.134192.168.237.1519491443ICMP24.8315194237218886.1428571.691399RSTHTTPSNormal1True051.4988210.00000011
52025-05-03 00:00:02172.19.102.8010.7.125.51834822ICMP18.29725345456181013.7333332.459386PSHSSHNormal1True052.493161300.11241913
62025-05-03 00:00:02172.18.107.43172.20.96.923195922TCP3.6411824227418652.80952411.534716URGSSHNormal1True057.5299730.00000011
72025-05-03 00:00:02192.168.87.167172.20.47.128530522UDP9.8867905231104598.1538465.259543FINSSHNormal1True053.146016221.20477717
82025-05-03 00:00:0354.38.236.14410.131.212.221672480ICMP21.2171435243064828.1538462.450848RSTHTTPNormal1True052.0296790.00000011
92025-05-03 00:00:03172.16.213.193192.168.232.1343930223ICMP0.9994575853486922.17241458.031500ACKTelnetNormal1True0553.515048218.18452212
timestampsource_ipdest_ipsource_portdest_portprotocoldurationpacketsbytesbytes_per_packetpackets_per_secondtcp_flagsserviceattack_stateseverity_scoreis_weekendhour_of_dayday_of_weekbytes_ratiopacket_size_varianceconnection_frequencyunique_ports_per_source
49999702025-06-01 23:59:5510.140.61.20010.25.153.775984880TCP10.2457615650719905.6964295.465675URGHTTPNormal1True2364.9502420.00000011
49999712025-06-01 23:59:5610.41.123.57192.168.143.2402656622TCP2.08680961730011196.73770529.231238URGSSHNormal1True23634.9821240.00000011
49999722025-06-01 23:59:5610.253.208.160192.168.14.433395653ICMP3.4707955554296987.20000015.846515FINDNSNormal1True23615.6436800.00000011
49999732025-06-01 23:59:57172.26.101.198192.168.13.123235080ICMP2.2429794030983774.57500017.833423RSTHTTPNormal1True23613.81332442.32140414
49999742025-06-01 23:59:5710.198.111.77192.168.14.7422141443UDP3.1278874640977890.80434814.706413SYNHTTPSNormal1True23613.1005360.00000011
49999752025-06-01 23:59:58172.27.233.46192.168.152.3210302443ICMP13.41044449634541294.9795923.653868PSHHTTPSNormal1True2364.731685334.14846313
49999762025-06-01 23:59:58172.28.32.168172.27.106.221519101433ICMP17.9027385232720629.2307692.904584PSHMSSQLNormal1True2361.827653230.65279312
49999772025-06-01 23:59:5898.103.130.135192.168.45.2443800180TCP7.13537649525591072.6326536.867192ACKHTTPNormal1True2367.3659740.00000011
49999782025-06-01 23:59:59192.168.146.88172.30.74.986126453ICMP3.01830543473271100.62790714.246406FINDNSNormal1True23615.679992193.03219917
49999792025-06-01 23:59:5910.146.115.182172.23.229.1995303225ICMP29.0120964325311588.6279071.482140URGSMTPNormal1True2360.8724290.00000011